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KOMPARASI ALGORITMA C4.5 DAN SVM BERBASIS PARTICLE SWARM OPTIMAZATION DALAM PENENTUAN KREDIT
Abstract
Bad credit is one of the credit risk faced by the financial and banking industry. Bad credit happens if in the long run, financial institutions or banks can not attract loans within a predetermined time. Bad credit has a negative effect on credit providers in the form of risk of loss course this should not be allowed to drag on and had to find a way out. However, to ensure accuracy in the determination of credit worthiness required an accurate algorithm. Therefore, there should be a study that aims to find an algorithm that accurately by means mengkomparasi some of them C4.5 algorithm, and SVM. To further improve the accuracy of the algorithms are in Particle Swarm Optimization with Optimazation. Berupan confusion matrix research results prove the accuracy of Support Vector Machine-based Particle Swarm Optimazation exists at the level of accuracy of 96.20% and the AUC by 0989.
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DOI: https://doi.org/10.31294/p.v18i1.877
Copyright (c) 2016 Syaifur Rahmatullah
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
ISSN: 2579-3500
